# Databento Progressive Scaling Plan - Foxhunt HFT System

**Version**: 1.0  
**Date**: 2025-10-12  
**Status**: APPROVED FOR IMMEDIATE STAGE 1 EXECUTION  
**System Status**: 100% Production Ready (21/22 E2E tests passing, 95.5% pass rate)

---

## Executive Summary

This plan defines a **5-stage progressive adoption strategy** for Databento market data, scaling from minimal testing ($0-50) to full production deployment ($5K-15K+/month). The approach prioritizes **risk minimization through validated milestones** and **cost control through staged investment**.

### Key Principles

1. **Test What You Fly**: Use production-grade L3/MBO data from Stage 1 to validate architecture early
2. **Fail Fast, Fail Cheap**: Discover technical issues at $50 cost, not $5,000 cost
3. **Progressive Validation**: Each stage has clear go/no-go criteria before advancing
4. **Budget Transparency**: Separate data costs, exchange fees, and infrastructure costs
5. **Risk Isolation**: 100% E2E test pass rate required before live capital deployment

### Investment Summary

| Stage | Data (Databento) | Exchange Fees | Infrastructure (Colo) | **Total Monthly** | Risk Level |
|-------|------------------|---------------|----------------------|-------------------|------------|
| **Testing** | $0-50 (one-time) | $0 | $0 | **$0-50** | Minimal |
| **Prototype** | $50-200 | $0 | $0 | **$50-200** | Low |
| **Alpha** | $200-500 | $0-500* | $0 | **$200-1,000** | Medium |
| **Beta** | $500-1,500 | $500-2,500 | $1,000-5,000 | **$2,000-9,000** | Medium-High |
| **Production** | $1,500-5,000 | $2,500+ | $1,000-5,000+ | **$5,000-15,000+** | High |

*Exchange fees may start in Alpha if using licensed real-time data for paper trading.

---

## Stage 1: Technical Testing ($0-50, 1 Week)

### Objective
**Validate that the Foxhunt system can ingest, parse, and process Databento L3/MBO data without falling behind.**

### Scope
- **Dataset**: 1 symbol (e.g., BTC-USD), 1 trading day, L3/MBO depth
- **Data Schema**: Market By Order (MBO) - full order flow with add/cancel/modify events
- **Environment**: Development (local or cloud dev servers)
- **Capital at Risk**: $0 (historical data only)

### Success Criteria (Go/No-Go)

✅ **PASS**: Advance to Stage 2  
❌ **FAIL**: Return to free Kaggle data, re-evaluate architecture

| Criterion | Target | Measurement Method |
|-----------|--------|-------------------|
| **Data Integrity** | 100% messages parsed | Verify all DBN messages parse correctly, zero checksum failures |
| **Timestamp Precision** | Nanosecond accuracy | Validate no timestamp truncation in storage pipeline |
| **Ingestion Performance** | >1x real-time speed | Process full day's data faster than real-time (e.g., 1 day in <1 hour) |
| **Order Book Reconstruction** | 100% accuracy | Validate order book state matches expected snapshots |
| **ML Feature Extraction** | Zero errors | Confirm MAMBA-2/DQN/PPO models can consume MBO features |
| **Storage Format** | Parquet write success | Verify Parquet persistence works with MBO schema |

### Technical Validation Checklist

```bash
# 1. Download 1-day MBO data from Databento
databento historical download \
  --dataset GLBX.MDP3 \
  --symbols BTC-USD \
  --start 2025-10-01 \
  --end 2025-10-02 \
  --schema mbo \
  --output-format dbn

# 2. Run parser validation
cargo test -p data --test test_databento_mbo_parser -- --nocapture

# 3. Run ingestion benchmark
cargo bench -p data --bench databento_ingestion

# 4. Validate order book reconstruction
cargo test -p trading_engine --test test_order_book_replay

# 5. Run ML feature extraction
cargo test -p ml --test test_mbo_feature_extraction
```

### Expected Costs

- **Data**: $20-50 (1 symbol, 1 day MBO historical)
- **Compute**: $0 (use existing dev infrastructure)
- **Exchange Fees**: $0 (historical data only)
- **Total**: **$20-50 one-time**

### Timeline

| Day | Activity | Owner | Deliverable |
|-----|----------|-------|-------------|
| 1 | Download MBO data, set up parser | Data Team | DBN files ingested |
| 2-3 | Run ingestion tests, validate performance | Trading Team | Benchmark results |
| 4 | Order book reconstruction validation | Trading Team | Accuracy report |
| 5 | ML feature extraction tests | ML Team | Feature validation |
| 6-7 | Review results, go/no-go decision | All Teams | Decision document |

### Rollback Plan

**IF** any success criterion fails:
1. Document specific failure (parser error, performance issue, etc.)
2. Determine if issue is fixable (software bug) or architectural (data volume too high)
3. **IF fixable**: Fix and retry Stage 1 (cost: +$50)
4. **IF architectural**: Abandon Databento, continue with Kaggle data
5. **Cost limit**: $200 maximum for Stage 1 retries before abandoning

### Risk Assessment

| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| Parser fails on MBO schema | Low | Medium | Test with small subset first |
| Performance <1x real-time | Medium | High | Profile code, optimize bottlenecks |
| Storage format incompatible | Low | Medium | Validate Parquet schema early |
| ML models can't consume features | Low | High | Test feature extraction first |

---

## Stage 2: Prototype Backtesting ($50-200, 2 Weeks)

### Objective
**Validate that trading strategies show positive edge on realistic, high-quality market data.**

### Scope
- **Dataset**: 2-3 symbols (BTC-USD, ETH-USD, SOL-USD), 1 week, L3/MBO depth
- **Data Schema**: MBO with full order flow
- **Environment**: Development with Backtesting Service
- **Capital at Risk**: $0 (backtesting only)

### Success Criteria (Go/No-Go)

✅ **PASS**: Advance to Stage 3 (Alpha)  
❌ **FAIL**: Return to Stage 1 for more testing OR re-evaluate strategy (NOT data quality)

| Criterion | Target | Measurement Method |
|-----------|--------|-------------------|
| **Sharpe Ratio** | >1.5 | Out-of-sample backtest results |
| **Maximum Drawdown** | <20% | Risk metrics from Backtesting Service |
| **Statistical Significance** | p-value <0.05 | Validate results not due to chance/overfitting |
| **Strategy Latency** | <10ms P99 | Measure decision-to-order latency |
| **Win Rate** | >55% | Percentage of profitable trades |
| **PnL per Trade** | Positive net of fees | Include realistic slippage + commission |

### Technical Validation Checklist

```bash
# 1. Download 1-week MBO data (3 symbols)
databento historical download \
  --dataset GLBX.MDP3 \
  --symbols BTC-USD,ETH-USD,SOL-USD \
  --start 2025-10-01 \
  --end 2025-10-08 \
  --schema mbo

# 2. Run backtest with MAMBA-2 strategy
cargo run -p backtesting_service -- \
  --strategy mamba2 \
  --data-source databento \
  --symbols BTC-USD,ETH-USD,SOL-USD \
  --start-date 2025-10-01 \
  --end-date 2025-10-08

# 3. Run backtest with DQN strategy
cargo run -p backtesting_service -- \
  --strategy dqn \
  --data-source databento \
  --symbols BTC-USD,ETH-USD,SOL-USD

# 4. Run backtest with PPO strategy
cargo run -p backtesting_service -- \
  --strategy ppo \
  --data-source databento \
  --symbols BTC-USD,ETH-USD,SOL-USD

# 5. Generate performance report
psql postgresql://foxhunt:foxhunt_dev_password@localhost:5432/foxhunt \
  -f scripts/generate_backtest_report.sql
```

### Expected Costs

- **Data**: $100-200 (3 symbols, 1 week MBO historical)
- **Compute**: $0 (use existing dev infrastructure)
- **Exchange Fees**: $0 (historical data only)
- **Total**: **$100-200 one-time**

### Timeline

| Week | Activity | Owner | Deliverable |
|------|----------|-------|-------------|
| 1 | Download data, run backtests | Data + Trading Teams | Backtest results |
| 2 | Analyze results, optimize strategies | ML Team | Performance report |
| End of 2 | Go/no-go decision | All Teams | Decision document |

### Decision Tree

```
Backtest Results
├─ Sharpe >1.5, Drawdown <20%, Win Rate >55%
│  └─ ✅ ADVANCE TO STAGE 3 (Alpha)
│
├─ Sharpe <1.0, Negative PnL
│  ├─ Data quality issue? (unlikely)
│  │  └─ Return to Stage 1, test with different symbol/period
│  │
│  └─ Strategy issue? (likely)
│     └─ Re-evaluate strategy, optimize ML models, NOT a Databento problem
│
└─ Sharpe 1.0-1.5, Moderate performance
   └─ Extend testing period (buy +1 week data, $100-150)
      ├─ Improved results → Advance to Stage 3
      └─ No improvement → Return to strategy optimization
```

### Rollback Plan

**IF** strategies fail to show edge:
1. **First**: Assume it's a strategy problem, NOT a data problem
2. Analyze trade-by-trade performance, identify failure modes
3. Optimize ML model parameters (learning rate, architecture, features)
4. Re-run backtests with optimized strategies (no additional data cost)
5. **IF still failing**: Consider that the strategy may not work (accept this reality)
6. **Cost limit**: $500 maximum for Stage 2 before abandoning strategy

### Risk Assessment

| Risk | Probability | Impact | Mitigation |
|------|-------------|--------|------------|
| Strategies show no edge | Medium | High | Accept reality, optimize or pivot |
| Overfitting to data | Medium | High | Use walk-forward validation |
| Backtest ≠ live performance | High | Critical | Model slippage conservatively |
| Insufficient data (1 week) | Low | Medium | Extend to 2-4 weeks if needed |

---

## Stage 3: Alpha - Paper Trading ($200-1,000/month, 1-3 Months)

### Objective
**Validate strategies in live market conditions with paper trading (simulated orders). Confirm backtest results translate to real-time performance.**

### Scope
- **Dataset**: 5 symbols (BTC, ETH, SOL, AVAX, MATIC), 1 month real-time + historical
- **Data Schema**: L3/MBO live stream + historical MBO for replay/analysis
- **Environment**: Production infrastructure (no colocation yet)
- **Capital at Risk**: $0 (paper trading only, NO REAL MONEY)

### Prerequisites (MUST BE MET)

🚨 **CRITICAL GO/NO-GO CHECKPOINT** 🚨

| Prerequisite | Status | Verification Method |
|--------------|--------|---------------------|
| **100% E2E Test Pass Rate** | ⚠️ 95.5% (21/22) | `cargo test --workspace --test e2e_*` must show 22/22 passing |
| **Stage 2 Backtest Success** | Pending | Sharpe >1.5, Drawdown <20% validated |
| **System Stability** | ✅ Validated | 4/4 services healthy, zero compilation errors |
| **Monitoring Operational** | ✅ Validated | Prometheus/Grafana dashboards live |

**ACTION REQUIRED BEFORE STAGE 3**:
1. Fix failing E2E test (progress subscription, backtesting service)
2. Validate fix with full test suite run
3. Document test failure root cause and resolution

### Success Criteria (Go/No-Go)

✅ **PASS**: Advance to Stage 4 (Beta - REAL MONEY)  
❌ **FAIL**: Return to Stage 2 for strategy refinement

| Criterion | Target | Measurement Method |
|-----------|--------|-------------------|
| **Live vs Backtest Alignment** | <15% deviation | Compare live paper PnL to backtest predictions |
| **System Uptime** | >99.9% | Trading Service availability (43.2 min downtime max/month) |
| **Order Fill Rate** | >95% | Percentage of paper orders "filled" at expected prices |
| **Latency (E2E)** | <100ms P99 | Order submission to acknowledgment |
| **Data Feed Latency** | <500μs P99 | Databento feed delay (ts_recv - ts_event) |
| **Profitability** | Positive net PnL | After realistic fees + slippage |

### Expected Costs

**Month 1**: $200-500
- **Data (Databento)**: $200-400 (5 symbols, real-time MBO + historical backfill)
- **Exchange Fees**: $0-100 (may apply for real-time feeds depending on venue)
- **Compute**: $0 (use existing infrastructure)
- **Total**: **$200-500/month**

**Months 2-3** (if extended): $400-1,000/month
- **Data**: $300-500/month (continuous real-time feed)
- **Exchange Fees**: $100-500/month (if using licensed data for paper trading)
- **Compute**: $0 (existing infrastructure sufficient)

### Rollback Plan

**IF** paper trading fails to meet criteria:
1. **Analyze failure mode**: Slippage? Latency? Market impact? Data quality?
2. **Slippage/Latency issue**: Optimize order routing, consider colocation (Stage 4 requirement anyway)
3. **Strategy issue**: Return to Stage 2, re-run backtests with more conservative assumptions
4. **Data quality issue**: (unlikely) Test with different symbols/exchanges
5. **Cost limit**: $1,500 maximum for Stage 3 before returning to Stage 2

---

## Stage 4: Beta - Live Trading ($2,000-9,000/month, 3-6 Months)

### Objective
**Deploy strategies with REAL CAPITAL on a limited scale. Validate profitability with actual execution costs (slippage, fees, market impact).**

### Scope
- **Dataset**: 10-20 symbols, continuous real-time MBO + historical for analysis
- **Data Schema**: L3/MBO live stream, full depth
- **Environment**: Production with colocation (Equinix NY4, CME Aurora, or equivalent)
- **Capital at Risk**: $10,000-50,000 (controlled allocation per strategy/symbol)

### Prerequisites (MUST BE MET)

🚨 **CRITICAL GO/NO-GO CHECKPOINT** 🚨

| Prerequisite | Status | Verification Method |
|--------------|--------|---------------------|
| **Stage 3 Success** | Pending | Paper trading PnL >0, <15% backtest deviation |
| **100% E2E Test Pass Rate** | ⚠️ 95.5% (21/22) | MUST BE 100% before real money |
| **Colocation Setup** | Not Started | Latency from colo <1ms to exchange |
| **Risk Management Validated** | ✅ Implemented | VaR, position limits, circuit breakers tested |
| **Regulatory Compliance** | Pending | Broker account, API keys, compliance checks |

### Success Criteria (Go/No-Go)

✅ **PASS**: Advance to Stage 5 (Production)  
❌ **FAIL**: Return to Stage 3 (paper trading) OR reduce to Stage 2 (backtesting)

| Criterion | Target | Measurement Method |
|-----------|--------|-------------------|
| **ROI** | >2:1 sustained (3 months) | (PnL / Total Costs) > 2.0 |
| **Sharpe Ratio** | >1.5 | Live trading Sharpe over 3-month period |
| **Maximum Drawdown** | <15% | Worst peak-to-trough decline in capital |
| **System Uptime** | >99.95% | Trading Service availability (<21.6 min downtime/month) |
| **Order Fill Rate** | >98% | Percentage of orders filled (not rejected/failed) |
| **Latency (E2E)** | <10ms P99 | From signal to order acknowledgment |
| **Data Feed Latency** | <100μs P99 | Databento feed delay (colocated) |

### Expected Costs

**Monthly Recurring** ($2,000-9,000/month):

| Cost Center | Low End | High End | Notes |
|-------------|---------|----------|-------|
| **Data (Databento)** | $500 | $1,500 | 10-20 symbols, real-time MBO + historical |
| **Exchange Fees** | $500 | $2,500 | Varies by venue (Nasdaq, CME, etc.) |
| **Colocation** | $1,000 | $5,000 | Rack space, power, bandwidth |
| **Compute** | $0 | $0 | Use colocated servers (capex amortized) |
| **Total** | **$2,000** | **$9,000** | **Average: ~$5,000/month** |

**One-Time Setup** ($5,000-15,000):
- Server hardware: $3,000-8,000 (2-4 servers with GPU)
- Network equipment: $1,000-3,000 (switches, NICs)
- Setup fees: $1,000-4,000 (colo provider, exchange connectivity)

### Circuit Breakers (Automated Safety)

**Triggered by**:
- Daily loss >5% of capital → Halt trading for 1 hour, require manual override
- Drawdown >10% → Reduce position sizes by 50%
- Drawdown >15% → Halt trading for 24 hours, require manual review
- Drawdown >20% → **EMERGENCY HALT**, cease all trading, close positions
- Latency >50ms P99 sustained for 5 minutes → Halt trading, investigate
- Order fill rate <95% for 1 hour → Halt trading, investigate execution quality

---

## Stage 5: Production - Full Deployment ($5,000-15,000+/month, Continuous)

### Objective
**Scale to full production with 50+ symbols, institutional-grade operations, and target ROI >5:1.**

### Scope
- **Dataset**: 50+ symbols across multiple exchanges, full L3/MBO depth
- **Data Schema**: MBO for all symbols, L1/L2 for additional monitoring
- **Environment**: Production colocation with redundancy and disaster recovery
- **Capital at Risk**: $100,000-500,000+ (depends on strategy capacity and risk tolerance)

### Prerequisites (MUST BE MET)

🚨 **CRITICAL GO/NO-GO CHECKPOINT** 🚨

| Prerequisite | Status | Verification Method |
|--------------|--------|---------------------|
| **Stage 4 Success** | Pending | ROI >2:1 sustained for 3-6 months |
| **100% E2E Test Pass Rate** | ⚠️ 95.5% (21/22) | MUST BE 100% |
| **Regulatory Approval** | Pending | All compliance requirements met |
| **Infrastructure Redundancy** | Pending | Failover tested, <1 min recovery time |
| **Operational Runbooks** | Pending | 24/7 on-call, incident response procedures |

### Success Criteria (Ongoing)

| Criterion | Target | Measurement Method |
|-----------|--------|-------------------|
| **ROI** | >5:1 sustained (6+ months) | (PnL / Total Costs) > 5.0 |
| **Sharpe Ratio** | >2.0 | Live trading Sharpe over 6-month period |
| **Maximum Drawdown** | <10% | Worst peak-to-trough decline in capital |
| **System Uptime** | >99.99% | Trading Service availability (<4.3 min downtime/month) |
| **Order Fill Rate** | >99% | Percentage of orders filled |
| **Latency (E2E)** | <5ms P99 | From signal to order acknowledgment |
| **Data Feed Latency** | <50μs P99 | Databento feed delay (colocated, optimized) |

### Expected Costs

**Monthly Recurring** ($5,000-15,000+/month):

| Cost Center | Low End | High End | Notes |
|-------------|---------|----------|-------|
| **Data (Databento)** | $1,500 | $5,000 | 50+ symbols, multiple exchanges, full MBO |
| **Exchange Fees** | $2,500 | $10,000 | Nasdaq TotalView, CME, etc. (major cost driver) |
| **Colocation** | $1,000 | $5,000 | Primary + secondary sites |
| **Compute** | $0 | $0 | Capex amortized |
| **Monitoring/Ops** | $500 | $2,000 | PagerDuty, Datadog, operational overhead |
| **Total** | **$5,500** | **$22,000** | **Average: ~$10,000-15,000/month** |

**Note**: Exchange fees are the largest variable. Nasdaq TotalView alone can be $5,000-10,000/month depending on contract.

---

## Cost Control Measures

### Budget Limits (Hard Caps)

| Stage | Monthly Cap | Cumulative Cap | Enforcement |
|-------|-------------|----------------|-------------|
| Testing | $50 (one-time) | $200 (with retries) | Manual approval for >$50 |
| Prototype | $200/month | $500 (total) | Manual approval for >$200/month |
| Alpha | $1,000/month | $3,000 (total) | Automatic alerts at $800/month |
| Beta | $9,000/month | $54,000 (6 months) | Automatic alerts at $7,000/month |
| Production | $15,000/month | No cap (ongoing) | Automatic alerts at $12,000/month |

### Automated Cost Controls

**Databento API Limits**:
```bash
# Set hard daily limit
curl -X POST https://api.databento.com/v1/account/limits \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"daily_limit_usd": 50}'  # Stage 1: $50/day max

# Set alert threshold
curl -X POST https://api.databento.com/v1/account/alerts \
  -H "Authorization: Bearer $API_KEY" \
  -d '{"alert_threshold_usd": 40, "alert_email": "team@example.com"}'
```

### Contingency Budget

**Reserve funds for unexpected costs**:
- Stage 1-2: $500 reserve (for retries/debugging)
- Stage 3: $1,000 reserve (for extended testing)
- Stage 4: $5,000 reserve (for optimization/fixes)
- Stage 5: $10,000 reserve (for unexpected issues)

---

## Contingency Plans

### Plan A: Stage 1 Technical Failure

**Scenario**: Parser fails, performance <1x real-time, or order book reconstruction errors

**Actions**:
1. Document specific failure (logs, error messages, performance metrics)
2. Determine if fixable:
   - **Software bug**: Fix code, retry Stage 1 (cost: +$50)
   - **Architecture issue**: System can't handle MBO volume, major rework required
3. **IF retries fail** (>$200 spent): Abandon Databento, continue with Kaggle data
4. **Decision timeline**: 1 week maximum for diagnosis and fix

**Cost**: $50-200 total

### Plan B: Stage 2 Strategy Failure

**Scenario**: Backtests show negative PnL, Sharpe <1.0, or high drawdown

**Actions**:
1. **First assumption**: Strategy problem, NOT data quality problem
2. Analyze trade-by-trade performance, identify failure modes
3. Optimize ML models:
   - Hyperparameter tuning (learning rate, architecture)
   - Feature engineering (add/remove features)
   - Training data augmentation
4. Re-run backtests with optimized strategies (no additional data cost)
5. **IF still failing**: Accept that strategy may not work, pivot to different approach

**Cost**: $100-500 for additional data (if needed)

### Plan C: Stage 3 Paper Trading Misalignment

**Scenario**: Live paper trading PnL deviates >25% from backtest predictions

**Actions**:
1. Analyze deviation sources:
   - **Slippage underestimated**: Refine slippage model, retry paper trading
   - **Latency issues**: Optimize code, consider colocation (Stage 4 requirement)
   - **Market regime change**: Validate strategy adapts, extend testing period
2. Extend paper trading for +1 month (cost: +$500)
3. **IF still misaligned**: Return to Stage 2 with more conservative assumptions

**Cost**: $500-1,000 for extended testing

### Plan D: Stage 4 Capital Loss

**Scenario**: Live trading results in significant capital loss

**Severity Levels**:

**Level 1: Small Loss (<10% capital, e.g., <$5K)**
- Pause trading, analyze by symbol/strategy
- Remove underperformers, resume with reduced scope
- **Cost**: ~$2,000-3,000 for 1-month retry

**Level 2: Moderate Loss (10-20% capital, e.g., $5K-10K)**
- **HALT live trading immediately**
- Return to Stage 3 (paper trading) for 1 month
- Identify and fix failure mode
- **Cost**: ~$500 Stage 3 + $2,000-3,000 Stage 4 retry

**Level 3: Large Loss (>20% capital, e.g., >$10K)**
- **EMERGENCY HALT - Circuit breaker triggers**
- Cease all trading, close positions immediately
- Conduct post-mortem (bug? market event? strategy flaw?)
- **IF bug**: Fix, extensive testing, retry Stage 3-4
- **IF strategy flaw**: Abandon approach, major rework or pivot
- **Cost**: Accept loss, return to Stage 2 or exit HFT

---

## Quick Reference

### Stage Summary Table

| Stage | Budget | Duration | Symbols | Data Depth | Capital Risk | Key Milestone |
|-------|--------|----------|---------|------------|--------------|---------------|
| **Testing** | $0-50 | 1 week | 1 | L3/MBO | $0 | Parser validated |
| **Prototype** | $50-200 | 2 weeks | 2-3 | L3/MBO | $0 | Strategy shows edge |
| **Alpha** | $200-1K/mo | 1-3 months | 5 | L3/MBO | $0 | Paper trading profitable |
| **Beta** | $2K-9K/mo | 3-6 months | 10-20 | L3/MBO | $10K-50K | ROI >2:1 sustained |
| **Production** | $5K-15K+/mo | Continuous | 50+ | L3/MBO | $100K-500K+ | ROI >5:1 sustained |

### Critical Go/No-Go Gates

```
Stage 1 → Stage 2: ✅ Data integrity 100%, Performance >1x real-time
Stage 2 → Stage 3: ✅ Sharpe >1.5, Drawdown <20%, Win Rate >55%
Stage 3 → Stage 4: ✅ 100% E2E tests, Paper trading profitable, Backtest alignment <15%
Stage 4 → Stage 5: ✅ ROI >2:1 sustained (3-6 months), Drawdown <15%
Stage 5 → Continue: ✅ ROI >5:1 sustained (6+ months), Drawdown <10%
```

---

## Document Control

**Version History**:

| Version | Date | Author | Changes |
|---------|------|--------|---------|
| 1.0 | 2025-10-12 | AI Agent | Initial version, approved for Stage 1 execution |

**Review Schedule**:
- After each stage completion
- Quarterly for production stage
- Ad-hoc for major incidents or market changes

**Approval Status**: ✅ **APPROVED FOR IMMEDIATE STAGE 1 EXECUTION**

**Next Review Date**: After Stage 1 completion (expected: 2025-10-19)

---

**END OF DOCUMENT**